Electronic Journal of Linear Algebra ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 26, pp. 574-589, August 2013 ELA ON EUCLIDEAN DISTANCE MATRICES OF GRAPHS∗ GASPERˇ JAKLICˇ † AND JOLANDA MODIC‡ Abstract. In this paper, a relation between graph distance matrices and Euclidean distance matrices (EDM) is considered. It is proven that distance matrices of paths and cycles are EDMs. The proofs are constructive and the generating points of studied EDMs are given in a closed form. A generalization to weighted graphs (networks) is tackled. Key words. Graph, Euclidean distance matrix, Distance, Eigenvalue. AMS subject classifications. 15A18, 05C50, 05C12. n n 1. Introduction. A matrix D R × is a Euclidean distance matrix (EDM), ∈ if there exist points x Rr, i =1, 2,...,n, such that d = x x 2. The minimal i ∈ ij k i − j k possible r is called an embedding dimension (see, e.g., [3]). Euclidean distance matri- ces were introduced by Menger in 1928, later they were studied by Schoenberg [13], and other authors. They have many interesting properties, and are used in various applications in linear algebra, graph theory, and bioinformatics. A natural problem is to study configurations of points xi, where only distances between them are known. n n Definition 1.1. A matrix D = (d ) R × is hollow, if d = 0 for all ij ∈ ii i =1, 2,...,n. There are various characterizations of EDMs. n n Lemma 1.2. [8, Lemma 5.3] Let a symmetric hollow matrix D R × have only ∈ one positive eigenvalue λ and the corresponding eigenvector e := [1, 1,..., 1]T Rn. 1 ∈ Then D is EDM. We will frequently use the following theorem. n n Theorem 1.3. [8, Theorem 2.2] Let D R × be a nonzero symmetric hollow ∈ matrix. Then D is EDM if and only if it has exactly one positive eigenvalue and there exists w Rn such that Dw = e and wT e 0. ∈ ≥ ∗Received by the editors on November 13, 2011. Accepted for publication on July 8, 2013. Handling Editor: Bryan L. Shader. †FMF and IMFM, University of Ljubljana, Jadranska 19, 1000 Ljubljana, Slovenia, and IAM, University of Primorska, Slovenia ([email protected]). ‡XLAB d.o.o., Pot za Brdom 100, 1125 Ljubljana, Slovenia, and FMF, University of Ljubljana, Slovenia ([email protected]). 574 Electronic Journal of Linear Algebra ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 26, pp. 574-589, August 2013 ELA Euclidean Distance Matrices of Graphs 575 In order to obtain generating points xi for an EDM matrix D, one can take a look at the singular value decomposition of Gower matrix 1 (1.1) F = I esT D I seT , −2 − − where s is a vector such that sT e = 1 (see [1]). Since F is positive semidefinite, it can T T T be written as F = X X with X = diag(√σi) U , where F = UΣU is the singular value decomposition of F and Σ = diag(σi). The points xi are obtained as columns of X. An EDM matrix D is circum-Euclidean (CEDM) (also spherical) if there exists a realization of its generating points xi that lie on the surface of some hypersphere [14]. Circum-Euclidean distance matrices are important because every EDM is a limit of CEDMs. This is analogous to the case of the cone of positive semidefinite matrices, where the interior of the cone consists of positive definite matrices. CEDMs can be characterized by the following result. n n Theorem 1.4. [14, Theorem 3.4] A Euclidean distance matrix D R × is ∈ CEDM if and only if there exist s Rn and β R, such that Ds = βe and sT e =1. ∈ ∈ If we choose s = 1/n e, the generating points of a CEDM lie on a hypersphere with center 0 and radius R = β/2. A nonzero EDM has only onep positive eigenvalue λ1, and the sum of its eigenvalues is zero. It is conjectured that any set of numbers that meet these conditions can be a spectrum of an EDM (see, e.g., [10, 11]). In this paper we will study distance matrices, where the graph distance is used. Our goal is to cover basic graphs, paths and cycles, and give constructive proofs that their distance matrices are EDMs. This will enable study of more complex graphs and networks. A generalization to weighted graphs is considered. Here, instead of the graph distance, the minimal sum of edge weights over all possible paths between two vertices is used. Thus the problem considered becomes much harder. Similar problems were studied in several papers. Line distance matrices were considered in [9, 12], cell matrices were introduced in [9], and distance matrices of weighted trees were studied in [2, 4]. The structure of the paper is as follows. In the next section, line distance matrices are presented and their relation to paths is analysed. In Section 3, we will apply the eigendecomposition of circulant matrices to study cycles and prove that their distance matrices are EDM. In Section 4, distance matrices of weighted cycles are considered. The paper is concluded by some remarks and ideas for future work. Electronic Journal of Linear Algebra ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 26, pp. 574-589, August 2013 ELA 576 G. Jakliˇcand J. Modic 2. Paths. A graph distance matrix Dp for a path P is defined by d = i n n ij | − j , i,j =1, 2,...,n. Thus it is a symmetric Toeplitz matrix, generated by its first row | 0, 1, 2,...,n 1. − A line distance matrix for parameters t <t < <t is defined as L = [ℓ ] 1 2 ··· n ij i,j with ℓ = t t (see [12]). Hence Dp is a line distance matrix defined by the ij | i − j | n sequence ti = i, i = 1, 2,...,n. In [9] it was proven that line distance matrices are CEDMs. Let L be a line distance matrix, associated with t1 < t2 < < tn. The matrix T ··· X = [x1, x2,..., xn], xi := [xi,1, xi,2,...,xi,n] , where 1 √t t , j < i, 2 j+1 − j 1 (2.1) xi,j = √t t , i j < n, − 2 j+1 − j ≤ 0, j = n, is a realization matrix for L, i.e., its columns are generating points for EDM L. Its generating points x lie on the hypersphere with center 0 and radius R = 1 √t t . i 2 n − 1 Since Dpn is a line distance matrix, one can see that Dpn is CEDM with generat- ing points (2.1), lying on the hypersphere around the origin with radius R = 1 √n 1 2 − and ti = i. Now let us consider weighted paths, i.e., paths Pn with edge weights wi > 0,i = w 1, 2,...,n 1. Let w := [w1, w2,...,wn 1] and let D pn denote the weighted path − − distance matrix. Here a path between every two vertices vi, vj ,i<j, is unique, and its (generalized) distance is equal to the sum of weights wi + wi+1 + + wj 1. A w ··· − weighted path distance matrix D pn is a line distance matrix defined by the sequence i w ti := wk 1, i =1, 2,...,n, where w0 := 0. Thus the matrix D pn is CEDM. k=1 − P 3. Cycles. A cycle is a graph Cn on n vertices consisting of a single closed directed walk. Let V (G) denote the set of vertices of a graph G. Let us order vertices successively, and let us define the distance d(u, v) between vertices u, v V (C ) as ∈ n their graph distance, i.e., the length of the shortest path between them. Let us define the distance matrix Dn := [d(u, v)]u,v V (Cn). For example, for the cycle C5 we obtain ∈ the distance matrix 01221 10122 D5 = 21012 . 22101 12210 Electronic Journal of Linear Algebra ISSN 1081-3810 A publication of the International Linear Algebra Society Volume 26, pp. 574-589, August 2013 ELA Euclidean Distance Matrices of Graphs 577 Clearly, the matrix Dn is a circulant matrix (see [5]), generated by its first row c0,c1,...,cn 1, where we need to consider two possibilities for elements ci: − n 1 n 1 n 1 0, 1,..., − , − , − 1,..., 1, n odd, 2 2 2 − n n n 0, 1,..., 1, , 1,..., 1, n even. 2 − 2 2 − Theorem 3.1. The distance matrix Dn of a cycle is CEDM. Proof. Since Dn is a circulant matrix, it is well known (see, e.g., [5]) that it has eigenvalues n 1 − λ = c ωℓ, j =0, 1,...,n 1, j+1 ℓ j − Xℓ=0 and the corresponding eigenvectors 2 n 1 T (3.1) vj+1 := [1, wj , wj ,...,wj − ] , where ωj := exp(i 2πj/n). Therefore, n 1 − 2n2 (1 ( 1)n) λ = c = − − − 1 i 8 i=1 X T and v1 = e = [1, 1,..., 1] . The symmetry in coefficients ci and the facts that n k k ωj − = ωj and k k 2kjπ ω + ω− = 2cos j j n yield n n−1 1+(−1) 2 − 4 2kjπ n (3.2) λ = 2k cos + (1 + ( 1)n) cos jπ = j+1 n − 4 kX=1 1 jπ 1 n 1 + ( 1)j cos + (1 + ( 1)n) cos jπ. 2 − − n sin2 jπ − 4 n For n odd, we obtain 1 j jπ 1 λj+1 = 1 + ( 1) cos . 2 − − n sin2 jπ n Here the eigenvalues λj , j =2, 3,...,n are obviously negative.